Combinatorial Data Analysis: Optimization by Dynamic Programming (Monographs on Discrete Mathematics and Applications)
|Rating||:||4.79 (938 Votes)|
|Number of Pages||:||175 Pages|
Excellent introduction to CDA for cluster analysis etc. Jeffrey Scargle I wanted to learn about combinatorial optimization for a particular application in cluster analysis, and this book hit the mark. This is a clearly written overview of the application of general dynamic programming to cluster analysis, object sequencing and seriation, and other data analysis problems "in which the arrangement of a collection of objects is absolutely central." [from the Preface]
From the Publisher Audience Combinatorial Data Analysis: Optimization by Dynamic Programming provides an applied documentation source, as well as an introduction to a collection of associated computer programs, that will be of interest to applied statisticians and data analysts as well as notationally sophisticated users.
Lawrence Hubert is Professor of Psychology and Statistics at the University of Illinois at Urbana-Champaign, USA; Phipps Arabie is Professor of Management and Psychology at Rutgers University, USA; Jacqueline Meulman is Professor of Applied Data Theory in the Faculty of Social and Behavioral Sciences of Leiden University, The Netherlands.
Second, the paradigm can lead directly to many more novel uses. First, the paradigm can be applied in various special forms to encompass all previously proposed applications suggested in the classification literature. An appendix is included as a user's manual for a collection of programs available as freeware.. Combinatorial data analysis (CDA) refers to a wide class of methods for the study of relevant data sets in which the arrangement of a collection of objects is absolutely central. The focus of this monograph is on the identification of arrangements, which are then further restricted to where the combinatorial search is carried out by a recursive optimization process based on the general princ